Hadoop & Big Data Foundations Mastery Course

Master Apache Hive through a structured journey from core concepts to advanced big data operations. You’ll begin by exploring Hive’s role in the Hadoop ecosystem and using HiveQL to create, manage, and modify databases and tables. You’ll then implement partitions and bucketing, perform inserts and overwrites, and manage large datasets efficiently. As you progress, you’ll apply inner, outer, skew, and map joins; configure SerDe for structured and semi-structured data; and use built-in and custom UDFs for transformation, filtering, and aggregation. You’ll also work with functions, expressions, sorting, clustering, sampling, MSCK repair, views, indexing, archiving, ranking, and variable substitution. Finally, you’ll examine Hive architecture, execution modes, table properties, query plans, compression, immutable tables, Slowly Changing Dimensions, and XML processing with SerDe and XPath. Designed for learners seeking practical Hive, data engineering, analytics, or Hadoop ecosystem skills, this course combines a step-by-step progression with hands-on queries and enterprise-focused data scenarios. Unlike a general SQL course, it focuses on Hive’s schema-on-read model, distributed query execution, and Hadoop scalability. Enroll to confidently design Hive data structures, analyze and transform big data, and optimize Hive query performance.

Master Apache Pig and learn to analyze, transform, and optimize large-scale datasets using Pig Latin. You’ll begin by exploring Apache Pig’s role in the Hadoop ecosystem, comparing it with Hive, and working with Local and MapReduce execution modes, Pig data types, and essential commands such as LOAD, DUMP, and STORE. As you progress, you’ll use GROUP, COGROUP, JOIN, UNION, SPLIT, FILTER, DISTINCT, and COUNT to integrate, partition, refine, and prepare data for analytics. You’ll then develop Pig Latin scripts, integrate them with HDFS for batch processing, interact with the Grunt Shell, examine execution plans with EXPLAIN, debug programs, and extend Pig through custom UDFs and Piggy Bank libraries. Designed for learners who want practical skills in big data processing, data transformation, and ETL workflows, this course offers a structured path from Apache Pig fundamentals to advanced programming. Its blend of scripting exercises and data transformation scenarios shows how Pig can simplify workflows compared with traditional MapReduce coding. By the end, you’ll be able to build, troubleshoot, and extend Apache Pig workflows that process and prepare large datasets efficiently.

Master Hadoop through a structured journey from Big Data foundations to advanced cluster administration. You’ll begin by identifying Big Data challenges and exploring the Hadoop ecosystem, HDFS write and read processes, and MapReduce programming through Word Count and output operations. As you progress, you’ll use the Cloudera environment, HDFS Web UI, HUE, and shell commands to interact with Hadoop. You’ll examine administrator responsibilities, Hadoop’s scalability and fault tolerance, and the roles of FS Image, NameNode, and Secondary NameNode. You’ll also explore HDFS architecture, block placement, Hadoop installation, hostnames, gateways, SSH keys, and password-less cluster communication. Finally, you’ll configure Hadoop site and slave files, apply rack awareness, use DFS administration tools, execute MapReduce jobs, and perform advanced HDFS file operations. You’ll validate system health, manage checkpointing, safe mode, maintenance mode, DataNode commissioning, and storage planning. Designed for learners seeking Hadoop development and administration skills, this course uniquely combines Big Data Hadoop, Hadoop Architecture and HDFS, and Hadoop on Cloudera in one practical learning path. Enroll to build the skills needed to configure clusters, process large datasets, and maintain reliable distributed systems.

Master distributed data processing with Hadoop and MapReduce through a structured progression from core concepts to advanced applications. You’ll begin by exploring key-value sorting, composite keys, partitioning, Hadoop commands, Word Count, combiners, and the integration of real-world datasets. As you progress, you’ll develop and run MapReduce jobs for movie rating analysis and user-based metrics, examine YARN architecture and NodeManager functionality, and submit JAR-based jobs on Hadoop clusters. You’ll then extend Word Count, process structured logs, use Pig scripts for complex data transformations, customize Java classes, and build inverted indexes for document retrieval. Finally, you’ll work with local MapReduce execution, SequenceFiles, weblog parsing, multi-stage analytics, indexing, and social graph datasets. You’ll deploy and test Hadoop jobs on Cloudera Local Host and integrate your skills through final projects and practice programs. Designed for beginners building a foundation and intermediate learners advancing their MapReduce programming skills, this course uniquely combines step-by-step demonstrations, applied datasets, and project-based practice. Enroll to gain practical experience designing, executing, validating, and deploying scalable data processing workflows within the Hadoop ecosystem.